Triple
T32281778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M. Loew |
E824713
|
entity |
| Predicate | employerOrStudio |
P180559
|
FINISHED |
| Object | Hollywood film studios |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hollywood film studios | Statement: [M. Loew, employerOrStudio, Hollywood film studios]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerOrStudio Context triple: [M. Loew, employerOrStudio, Hollywood film studios]
-
A.
employerOfCreator
Indicates that one entity is the employer of the entity that created another entity.
-
B.
employerOrPublisherOf
Indicates that one entity serves as the employer or publishing organization responsible for another entity (such as a person or work).
-
C.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
D.
employerInPlot
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
E.
employerOrPartner
Indicates that one entity is either the employer of, or a business partner with, another entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7431c0eec81909ead443e07d75e18 |
completed | May 3, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69f74143cf708190a12d487884298437 |
completed | May 3, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69f7431aac148190bb6aac59817c174a |
completed | May 3, 2026, 12:44 p.m. |
Created at: May 1, 2026, 12:43 a.m.